import sys
import numpy as np
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
import seaborn as sns
sc.settings.set_figure_params(dpi=120)
sns.set_style("dark")
from sklearn.svm import SVR
def filter_cv_vs_mean(S: np.ndarray, N: int, svr_gamma: float=None, plot: bool=True, min_expr_cells: int=2,
max_expr_avg: float=20, min_expr_avg: float=0) -> np.ndarray:
muS = S.mean(1)
detected_bool = ((S > 0).sum(1) > min_expr_cells) & (muS < max_expr_avg) & (muS > min_expr_avg)
Sf = S[detected_bool, :]
mu = Sf.mean(1)
sigma = Sf.std(1, ddof=1)
cv = sigma / mu
log_m = np.log2(mu)
log_cv = np.log2(cv)
if svr_gamma is None:
svr_gamma = 150. / len(mu)
svr = SVR(gamma=svr_gamma)
svr.fit(log_m[:, None], log_cv)
fitted_fun = svr.predict
ff = fitted_fun(log_m[:, None])
score = log_cv - ff
xnew = np.linspace(np.min(log_m), np.max(log_m))
ynew = svr.predict(xnew[:, None])
nth_score = np.sort(score)[::-1][N]
if plot:
plt.scatter(log_m[score > nth_score], log_cv[score > nth_score], s=3, alpha=0.4, c="tab:red")
plt.scatter(log_m[score <= nth_score], log_cv[score <= nth_score], s=3, alpha=0.4, c="tab:blue")
mu_linspace = np.linspace(np.min(log_m), np.max(log_m))
plt.plot(mu_linspace, fitted_fun(mu_linspace[:, None]), c="k")
plt.xlabel("log2 mean S")
plt.ylabel("log2 CV S")
cv_mean_score = np.zeros(detected_bool.shape)
cv_mean_score[~detected_bool] = np.min(score) - 1e-16
cv_mean_score[detected_bool] = score
cv_mean_selected = cv_mean_score >= nth_score
return cv_mean_selected
adata = sc.read_text("H9_RPE_YOUNG_RawCountMatrix.tsv.gz").T
adata
adata.obs['n_counts'] = adata.X.sum(axis=1)
n, bins, *x = plt.hist(adata.obs['n_counts'], bins=100)
plt.xlabel("Number of UMIs")
plt.ylabel("Number of cells")
plt.axvline(3000, c="r")
plt.axvline(np.median(adata.obs['n_counts']), c="black")
plt.axvline(12000, c="r")
plt.show()
sc.pp.filter_cells(adata, min_counts=3000)
sc.pp.filter_cells(adata, max_counts=12000)
sc.pl.highest_expr_genes(adata, n_top=20)
expressed_genes = np.sum(adata.X > 0, 1)
adata.obs['n_genes'] = expressed_genes
len(expressed_genes)
n, bins, *x = plt.hist(expressed_genes, bins=100)
plt.axvline(1000, c="r")
plt.axvline(3000, c="r")
plt.xlabel("Number of Genes")
plt.ylabel("Number of Cells")
plt.show()
adata = adata[adata.obs['n_genes'] > 1000, :].copy()
adata = adata[adata.obs['n_genes'] < 3000, :].copy()
mito_genes = adata.var_names.str.startswith('MT-')
adata.obs['percent_mito'] = np.sum(
adata[:, mito_genes].X, axis=1) / np.sum(adata.X, axis=1)
adata.obs['n_counts_filt'] = adata.X.sum(axis=1)
sc.pl.scatter(adata, x='n_counts', y='percent_mito')
adata = adata[adata.obs['percent_mito'] < 0.15].copy()
adata = adata[adata.obs['percent_mito'] > 0.02].copy()
adata
adata_raw = adata.copy()
S_genes_hum = ["MCM5", "PCNA", "TYMS", "FEN1", "MCM2", "MCM4", "RRM1", "UNG", "GINS2",
"MCM6", "CDCA7", "DTL", "PRIM1", "UHRF1", "CENPU", "HELLS", "RFC2",
"RPA2", "NASP", "RAD51AP1", "GMNN", "WDR76", "SLBP", "CCNE2", "UBR7",
"POLD3", "MSH2", "ATAD2", "RAD51", "RRM2", "CDC45", "CDC6", "EXO1", "TIPIN",
"DSCC1", "BLM", "CASP8AP2", "USP1", "CLSPN", "POLA1", "CHAF1B", "BRIP1", "E2F8"]
G2M_genes_hum = ["HMGB2", "CDK1", "NUSAP1", "UBE2C", "BIRC5", "TPX2", "TOP2A", "NDC80",
"CKS2", "NUF2", "CKS1B", "MKI67", "TMPO", "CENPF", "TACC3", "PIMREG",
"SMC4", "CCNB2", "CKAP2L", "CKAP2", "AURKB", "BUB1", "KIF11", "ANP32E",
"TUBB4B", "GTSE1", "KIF20B", "HJURP", "CDCA3", "JPT1", "CDC20", "TTK",
"CDC25C", "KIF2C", "RANGAP1", "NCAPD2", "DLGAP5", "CDCA2", "CDCA8", "ECT2",
"KIF23", "HMMR", "AURKA", "PSRC1", "ANLN", "LBR", "CKAP5", "CENPE",
"CTCF", "NEK2", "G2E3", "GAS2L3", "CBX5", "CENPA"]
sc.tl.score_genes_cell_cycle(adata, s_genes=S_genes_hum, g2m_genes=G2M_genes_hum)
sc.pp.filter_genes(adata, min_cells=20)
adata
sc.pp.normalize_total(adata)
cv_vs_mean_keep = filter_cv_vs_mean(adata.X.T, N=2000, max_expr_avg=50)
sc.pp.log1p(adata)
adata = adata[:, cv_vs_mean_keep].copy()
sc.pp.regress_out(adata, "percent_mito")
sc.pp.regress_out(adata, "n_counts")
sc.pp.scale(adata, max_value=10)
sc.tl.pca(adata, svd_solver='arpack')
sc.pl.pca(adata, color='PAX6')
sc.pl.pca_variance_ratio(adata, log=True)
sc.pp.neighbors(adata, n_neighbors=30, n_pcs=20)
sc.tl.umap(adata, alpha=0.3, min_dist=.5)
sc.tl.louvain(adata, resolution=0.75)
sc.pl.umap(adata, use_raw=False, color=["louvain", "percent_mito", "phase", "n_genes", "n_counts"], ncols=5)
adata.raw = adata_raw
sc.tl.rank_genes_groups(adata, 'louvain', method='wilcoxon', use_raw=True)
adata_raw_norm = adata_raw.copy()
adata_raw_norm
sc.pp.normalize_total(adata_raw_norm)
sc.pp.log1p(adata_raw_norm)
adata_raw_norm.obsm["X_umap"] = adata.obsm["X_umap"]
adata_raw_norm.obs["phase"] = [i for i in adata.obs["phase"]]
adata_raw_norm.obs["louvain"] = [i for i in adata.obs["louvain"]]
sc.pl.umap(adata_raw_norm, use_raw=False, color=["louvain", "percent_mito", "phase", "n_genes", "n_counts"], ncols=5)
sc.pl.umap(adata_raw_norm, use_raw=False, color=["RAX", "MITF", "BEST1", "RPE65", "PAX6"], ncols=5)
pd.DataFrame(adata.uns['rank_genes_groups']['names']).head(10)
sc.pl.umap(adata_raw_norm, use_raw=False, color=["louvain", "ACTA2"], ncols=5)
l2ct = {'0':"LateRPE", '1':"LateRPE", '2':"LateRPE",
'3':"MidRPE", '4':"LateRPE", '5':"EarlyRPE", '6':"LateRPE", '7':"LateRPE", '8':"RetProg"}
adata_raw_norm.obs["cell_type"] = [l2ct[i] for i in adata_raw_norm.obs["louvain"]]
sc.pl.umap(adata_raw_norm, use_raw=False, color=["louvain", "cell_type"], ncols=4)
adata_raw.obs["cell_type"] = adata_raw_norm.obs["cell_type"]
# Subclustering
adata = adata_raw.copy()
adata = adata[adata.obs["cell_type"]=="EarlyRPE"].copy()
S_genes_hum = ["MCM5", "PCNA", "TYMS", "FEN1", "MCM2", "MCM4", "RRM1", "UNG", "GINS2",
"MCM6", "CDCA7", "DTL", "PRIM1", "UHRF1", "CENPU", "HELLS", "RFC2",
"RPA2", "NASP", "RAD51AP1", "GMNN", "WDR76", "SLBP", "CCNE2", "UBR7",
"POLD3", "MSH2", "ATAD2", "RAD51", "RRM2", "CDC45", "CDC6", "EXO1", "TIPIN",
"DSCC1", "BLM", "CASP8AP2", "USP1", "CLSPN", "POLA1", "CHAF1B", "BRIP1", "E2F8"]
G2M_genes_hum = ["HMGB2", "CDK1", "NUSAP1", "UBE2C", "BIRC5", "TPX2", "TOP2A", "NDC80",
"CKS2", "NUF2", "CKS1B", "MKI67", "TMPO", "CENPF", "TACC3", "PIMREG",
"SMC4", "CCNB2", "CKAP2L", "CKAP2", "AURKB", "BUB1", "KIF11", "ANP32E",
"TUBB4B", "GTSE1", "KIF20B", "HJURP", "CDCA3", "JPT1", "CDC20", "TTK",
"CDC25C", "KIF2C", "RANGAP1", "NCAPD2", "DLGAP5", "CDCA2", "CDCA8", "ECT2",
"KIF23", "HMMR", "AURKA", "PSRC1", "ANLN", "LBR", "CKAP5", "CENPE",
"CTCF", "NEK2", "G2E3", "GAS2L3", "CBX5", "CENPA"]
sc.tl.score_genes_cell_cycle(adata, s_genes=S_genes_hum, g2m_genes=G2M_genes_hum)
sc.pp.filter_genes(adata, min_cells=20)
adata
sc.pp.normalize_total(adata)
cv_vs_mean_keep = filter_cv_vs_mean(adata.X.T, N=2000, max_expr_avg=50)
sc.pp.log1p(adata)
adata = adata[:, cv_vs_mean_keep].copy()
sc.pp.regress_out(adata, "percent_mito")
sc.pp.regress_out(adata, "n_counts")
sc.pp.scale(adata, max_value=10)
sc.tl.pca(adata, svd_solver='arpack')
sc.pl.pca(adata, color='PAX6')
sc.pl.pca_variance_ratio(adata, log=True)
sc.pp.neighbors(adata, n_neighbors=30, n_pcs=20)
sc.tl.umap(adata, alpha=0.3, min_dist=.5)
sc.tl.louvain(adata, resolution=1.25)
sc.pl.umap(adata, use_raw=False, color=["louvain", "percent_mito", "phase", "n_genes", "n_counts"], ncols=5)
l2ct = {'0':"EarlyRPE", '1':"EMT-RPE", '2':"EarlyRPE",
'3':"EarlyRPE", '4':"EarlyRPE", '5':"EarlyRPE", '6':"EMT-RPE", '7':"CyclingRPE"}
adata.obs["cell_type"] = [l2ct[i] for i in adata.obs["louvain"]]
bc2ct = {i:j for i,j in zip(adata.obs.index, adata.obs["cell_type"])}
ct = []
for i, t in zip(adata_raw_norm.obs.index, adata_raw_norm.obs["cell_type"]):
if i in bc2ct:
ct.append(bc2ct[i])
else:
ct.append(t)
adata_raw_norm.obs["cell_type"] = ct
sc.pl.umap(adata_raw_norm, color=["RLBP1", "cell_type"])
adata_raw.obs["cell_type"] = adata_raw_norm.obs["cell_type"]
adata_raw.obsm["X_umap"] = adata_raw_norm.obsm["X_umap"]
adata_raw_norm.obs["cell_type"].value_counts()/len(adata_raw_norm)*100
genes = ["PAX6", "CRABP1", "NRN1", "NBL1", "CPAMD8", "SIX6", "VSX2", "NCAM1", "SFRP2",
"MITF", "DCT", "TYRP1", "TYR", "RLBP1", "BEST1", "RPE65", "TTR", "RGR"]
genes = ["CRABP1", "VSX2", "NCAM1", "TOP2A", "MKI67", "CDK1",
"MITF", "TYRP1", "DCT",
"RLBP1", "BEST1", "RPE65"]
X = pd.DataFrame(adata_raw_norm[:, genes].X.toarray())
X.index = adata_raw_norm.obs["cell_type"]
X.columns = genes
X = X.groupby(X.index).mean()
X = X.loc[['RetProg', 'CyclingRPE', 'EarlyRPE', 'MidRPE', 'LateRPE', ]]
plt.figure(None, (16, 4))
sns.heatmap((X - X.min()) / (X.max() - X.min()), cmap='viridis')
plt.show()